Modern approaches for learning on dynamic graphs have adopted the use of
batches instead of applying updates one by one. The use of batches allows these
techniques to become helpful in streaming scenarios where updates to graphs are
received at extreme speeds. Using batches, however, forces the models to update
infrequently, which results in the degradation of their performance. In this
work, we suggest a decoupling strategy that enables the models to update
frequently while using batches. By decoupling the core modules of temporal
graph networks and implementing them using a minimal number of learnable
parameters, we have developed the Lightweight Decoupled Temporal Graph Network
(LDTGN), an exceptionally efficient model for learning on dynamic graphs. LDTG
was validated on various dynamic graph benchmarks, providing comparable or
state-of-the-art results with significantly higher throughput than previous
art. Notably, our method outperforms previous approaches by more than 20\% on
benchmarks that require rapid model update rates, such as USLegis or UNTrade.
The code to reproduce our experiments is available at
\href{this https URL}{this http url}.

动态图学习的现代方法应用批处理而非逐一更新，本研究提出了一种解耦策略以实现频繁更新，开发了轻量级解耦时态图网络模型 (LDTGN)，在各种动态图基准测试中验证了其较高效率和结果优于之前方法的性能。

利用模块解耦加强时态图网络

Leveraging Temporal Graph Networks Using Module Decoupling

Video panoptic segmentation is a challenging task that serves as the
cornerstone of numerous downstream applications, including video editing and
autonomous driving. We believe that the decoupling strategy proposed by DVIS
enables more effective utilization of temporal information for both "thing" and
"stuff" objects. In this report, we successfully validated the effectiveness of
the decoupling strategy in video panoptic segmentation. Finally, our method
achieved a VPQ score of 51.4 and 53.7 in the development and test phases,
respectively, and ultimately ranked 1st in the VPS track of the 2nd PVUW
Challenge. The code is available at this https URL

本文介绍了一种基于解耦策略的视频全景分割方法，该方法在开发和测试阶段分别达到了 51.4 和 53.7 的 VPQ 得分，并在第二届 PVUW 挑战赛的 VPS 赛道上排名第一。

PVUW 挑战赛 2023 年视频全景分割问题的第一名解决方案

1st Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation

Video instance segmentation (VIS) is a critical task with diverse
applications, including autonomous driving and video editing. Existing methods
often underperform on complex and long videos in real world, primarily due to
two factors. Firstly, offline methods are limited by the tightly-coupled
modeling paradigm, which treats all frames equally and disregards the
interdependencies between adjacent frames. Consequently, this leads to the
introduction of excessive noise during long-term temporal alignment. Secondly,
online methods suffer from inadequate utilization of temporal information. To
tackle these challenges, we propose a decoupling strategy for VIS by dividing
it into three independent sub-tasks: segmentation, tracking, and refinement.
The efficacy of the decoupling strategy relies on two crucial elements: 1)
attaining precise long-term alignment outcomes via frame-by-frame association
during tracking, and 2) the effective utilization of temporal information
predicated on the aforementioned accurate alignment outcomes during refinement.
We introduce a novel referring tracker and temporal refiner to construct the
\textbf{D}ecoupled \textbf{VIS} framework (\textbf{DVIS}). DVIS achieves new
SOTA performance in both VIS and VPS, surpassing the current SOTA methods by
7.3 AP and 9.6 VPQ on the OVIS and VIPSeg datasets, which are the most
challenging and realistic benchmarks. Moreover, thanks to the decoupling
strategy, the referring tracker and temporal refiner are super light-weight
(only 1.69\% of the segmenter FLOPs), allowing for efficient training and
inference on a single GPU with 11G memory. The code is available at
\href{https://github.com/zhang-tao-whu/DVIS}{this https URL}.

提出一种分离策略，并应用于视频实例分割任务，包括分割、跟踪和细化，使用引用跟踪器和时间细化器构建 Decoupled VIS 框架 (DVIS)，并在 OVIS 和 VIPSeg 数据集上取得了新的 SOTA 表现。